Supplementary Material A: R Code for statistical analysis of questionnaire data
2023-08-29
This RMarkdown shows the analysis of the questionnaire data using ANOVAs and correlations using R (version 4.3.1).
Loading R packages and importing dataset
library(tidyverse) # for cleaning up data / plotting
library(dplyr) # to manipulate data set
library(ggpubr) # to plot data
library(ggplot2) # to plot data
library(qqplotr) # to plot data
library(rstatix) # for calculating effect sizes t-tests
library(car) # for statistical analysis
library(lsr) # for statistical analysis
library(stats) # for statistical analysis
library(PermutationR)
library(sjstats)# set working directory, where the questionnaire_data.txt file is stored
setwd("C:/Users/lisab/Documents")
myData <- read.table("EEG_questionnaireN89.txt", header = T, dec = ".")
head(myData) ## slope sex age Task_2DvAR RvS PANAS_pos_pre PANAS_neg_pre
## aaai -0.242213503 2 19 2 1 3.1 1.2
## appa 0.652958229 2 21 2 1 3.8 1.0
## aqsd -0.282977520 2 30 2 2 3.6 1.1
## asdf 0.635896520 1 22 1 2 3.9 1.2
## balu -0.004130111 2 24 1 2 2.1 1.1
## bbbo 0.918664371 1 26 2 1 4.0 1.0
## PANAS_pos_post PANAS_neg_post PANAS_diff_pos PANAS_diff_neg SSQ_pre_N
## aaai 3.3 1 0.2 -0.2 12.26571
## appa 3.9 1 0.1 0.0 9.54000
## aqsd 4.0 1 0.4 -0.1 9.54000
## asdf 3.5 2 -0.4 0.8 10.90286
## balu 2.9 1 0.8 -0.1 12.26571
## bbbo 4.6 1 0.6 0.0 12.26571
## SSQ_pre_O SSQ_pre_D SSQ_pre_TS SSQ_post_N SSQ_post_O SSQ_post_D
## aaai 18.408571 21.87429 6.07750 11.11143 12.994286 15.90857
## appa 8.662857 13.92000 3.97375 10.54000 8.662857 13.92000
## aqsd 8.662857 13.92000 3.97375 10.68286 10.828571 13.92000
## asdf 10.828571 15.90857 4.44125 10.82571 11.911429 15.90857
## balu 14.077143 13.92000 5.14250 10.54000 10.828571 15.90857
## bbbo 9.745714 13.92000 4.44125 10.54000 9.745714 17.89714
## SSQ_post_TS SSQ_diff_N SSQ_diff_O SSQ_diff_D SSQ_diff_TS TUI_Neugierde
## aaai 5.37625 -1.15428571 -5.414286 -5.965714 -0.70125 3.50
## appa 3.97375 1.00000000 0.000000 0.000000 0.00000 5.25
## aqsd 4.44125 1.14285714 2.165714 0.000000 0.46750 4.50
## asdf 4.90875 -0.07714286 1.082857 0.000000 0.46750 4.25
## balu 4.44125 -1.72571429 -3.248571 1.988571 -0.70125 6.50
## bbbo 4.67500 -1.72571429 0.000000 3.977143 0.23375 7.00
## TUI_Technologieaengstlichkeit TUI_Interesse TUI_Benutzerfreundlichkeit
## aaai 2.00 3.50 4.000000
## appa 2.25 2.00 4.666667
## aqsd 1.00 2.00 3.666667
## asdf 1.75 2.25 3.666667
## balu 1.25 1.50 4.333333
## bbbo 1.25 4.50 5.000000
## TUI_Immersion TUI_Nuetzlichkeit TUI_Skepsis TUI_Zugaenglichkeit
## aaai 5.25 4.00 3.50 1.333333
## appa 4.75 4.00 2.25 4.333333
## aqsd 6.25 2.25 1.25 3.000000
## asdf 3.00 1.75 3.75 1.333333
## balu 5.75 1.50 1.00 1.666667
## bbbo 4.50 6.00 1.25 3.000000
## Flow_Glatter_Verlauf Flow_Absorbiertheit Flow_Besorgnis Flow_Total
## aaai 4.666667 4.25 1.000000 4.5
## appa 3.500000 4.25 1.333333 3.8
## aqsd 5.333333 6.00 1.000000 5.6
## asdf 3.333333 3.00 2.333333 3.2
## balu 4.833333 4.00 1.000000 4.5
## bbbo 6.833333 6.25 3.666667 6.6
## Kontrolle Konzentration Erfolg_Strategie Spass Pain Pressure_on_head
## aaai 7 7 9 10 1 5
## appa 4 9 3 9 0 1
## aqsd 3 9 5 10 0 13
## asdf 2 5 6 2 1 27
## balu 5 9 8 9 10 11
## bbbo 10 10 10 10 0 33
## Headache Eye_burning Neck_pain
## aaai 1 0 0
## appa 0 1 0
## aqsd 0 0 0
## asdf 14 1 0
## balu 0 0 1
## bbbo 0 0 1# Code: Participant code
# Slope: individual regression slopes with feedback run number as predictor and SMR power as criterion
# Sex: 1 = Male, 2 = Female
# Age: age of the participants in years
# Task_2DvAR: 1 = 2D, 2 = AR
# RvS: 1 = real feedback, 2 = sham feedback
# PANAS_pos_pre: PANAS subscale positive affect assessed before the task
# PANAS_neg_pre: PANAS subscale negative affect assessed before the task
# PANAS_pos_post: PANAS subscale positive affect assessed after the task
# PANAS_neg_post: PANAS subscale negative affect assessed after the task
# PANAS_diff_pos: PANAS subscale positive affect, difference value between post-pre
# PANAS_diff_neg: PANAS subscale negative affect, difference value between post-pre
# SSQ_pre_N: SSQ subscale nausea assessed before the task
# SSQ_pre_O: SSQ subscale oculomotor disturbance assessed before the task
# SSQ_pre_D: SSQ subscale disorientation assessed before the task
# SSQ_pre_TS: SSQ total score assessed before the task
# SSQ_post_N: SSQ subscale nausea assessed after the task
# SSQ_post_O: SSQ subscale oculomotor disturbance assessed after the task
# SSQ_post_D: SSQ subscale disorientation assessed after the task
# SSQ_post_TS: SSQ total score assessed after the task
# SSQ_diff_N: SSQ subscale nausea, difference value between post-pre
# SSQ_diff_O: SSQ subscale oculomotor disturbance, difference value between post-pre
# SSQ_diff_D: SSQ subscale disorientation, difference value between post-pre
# SSQ_diff_TS: SSQ total score, difference value between post-pre
# TUI_Neugierde: TUI subscale curiosity
# TUI_Technologieaengstlichkeit: TUI subsclae technology fear
# TUI_Interesse: TUI subscale interest
# TUI_Benutzerfreundlichkeit: TUI subscale usability
# TUI_Immersion: TUI subscale immersion
# TUI_Nuetzlichkeit: TUI subscale usefulness
# TUI_Skepsis: TUI subscale skepticism
# TUI_Zugaenglichkeit: TUI subscale accessibility
# Flow_Glatter_Verlauf: FKS Flow Short Scale - subscale "Fluency"
# Flow_Absorbiertheit: FKS FLow Short Scale - subscale "Absorption"
# Flow_Besorgnis: FKS Flow Short Scale - subscale "Concern"
# Flow_Total: FKS Flow Short Scale - subscale "General factor"
# Kontrolle: Feeling of control over the movement of the bars/flowers
# Konzentration: Level of concentration during task
# Erfolg_Strategie: Perceived success of used mental strategies used to control the movement of the bars/flowers
# Spass: Fun during the task
# Pain: Perceived pain when wearing the headset including AR system
# Pressure_on_head: Perceived pressure on the head when wearing the headset including AR system
# Headache: Perceived headache when wearing the headset including AR system
# Eye_burning: Perceived eye burning when wearing the headset including AR system
# Neck_pain: Neck pain when wearing the headset including AR system, 1 = yes, 0 = noPrepare dataset
myData$Kontrolle <- as.numeric(myData$Kontrolle)
myData$Konzentration <- as.numeric(myData$Konzentration)
myData$Erfolg_Strategie <- as.numeric(myData$Erfolg_Strategie)
myData$Spass <- as.numeric(myData$Spass)
#Designate Task_2DvAR and RvS as a categorical factor
myData$Task_2DvAR<-as.factor(myData$Task_2DvAR)
myData$RvS<-as.factor(myData$RvS)
Task_2D <- myData[myData[,"Task_2DvAR"]=="1",]
Task_AR <- myData[myData[,"Task_2DvAR"]=="2",]PANAS - Positive / Negative Affect
Checking for outliers using Box-Plots
plot_PANAS_diff_pos <- ggplot(myData,aes(Task_2DvAR,PANAS_diff_pos))+geom_boxplot(aes(fill=RvS)) +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference positive affect")+
theme(axis.text.x = element_text(color = "grey20", size = 16, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 16, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 20, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 20, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_PANAS_diff_pos <- ggplot(myData,aes(Task_2DvAR,PANAS_diff_pos))+geom_boxplot(aes(fill=RvS))
plot_PANAS_diff_pos
ggplot(myData,aes(Task_2DvAR,PANAS_diff_pos))+geom_boxplot(aes(fill=RvS))
plot_PANAS_diff_neg <- ggplot(myData, aes(factor(Task_2DvAR), PANAS_diff_neg, )) +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference negative affect")+
theme(axis.text.x = element_text(color = "grey20", size = 16, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 16, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 20, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 20, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_PANAS_diff_neg <- ggplot(myData,aes(Task_2DvAR,PANAS_diff_neg))+geom_boxplot(aes(fill=RvS))
plot_PANAS_diff_neg
ggarrange(plot_PANAS_diff_pos, plot_PANAS_diff_neg,
# labels = c("PANAS positive affect", "PANAS negative affect"),
ncol = 2, nrow = 1)
Removing outliers when Interquartile Range IQR > 1.5
PANAS_diff_pos_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_PANAS_diff_pos_2D <- boxplot(PANAS_diff_pos_2D$PANAS_diff_pos, plot = FALSE)$out
# Replace the values with NA
PANAS_diff_pos_2D[PANAS_diff_pos_2D$PANAS_diff_pos %in% outliers_PANAS_diff_pos_2D, "PANAS_diff_pos"] = NA
PANAS_diff_pos_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_PANAS_diff_pos_AR <- boxplot(PANAS_diff_pos_AR$PANAS_diff_pos, plot = FALSE)$out
# Replace the values with NA
PANAS_diff_pos_AR[PANAS_diff_pos_AR$PANAS_diff_pos %in% outliers_PANAS_diff_pos_AR, "PANAS_diff_pos"] = NA
myData_PANAS_diff_pos_noOutliers <- rbind(PANAS_diff_pos_2D, PANAS_diff_pos_AR)
PANAS_diff_neg_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_PANAS_diff_neg_2D <- boxplot(PANAS_diff_neg_2D$PANAS_diff_neg, plot = FALSE)$out
# Replace the values with NA
PANAS_diff_neg_2D[PANAS_diff_neg_2D$PANAS_diff_neg %in% outliers_PANAS_diff_neg_2D, "PANAS_diff_neg"] = NA
PANAS_diff_neg_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_PANAS_diff_neg_AR <- boxplot(PANAS_diff_neg_AR$PANAS_diff_neg, plot = FALSE)$out
# Replace the values with NA
PANAS_diff_neg_AR[PANAS_diff_neg_AR$PANAS_diff_neg %in% outliers_PANAS_diff_neg_AR, "PANAS_diff_neg"] = NA
myData_PANAS_diff_neg_noOutliers <- rbind(PANAS_diff_neg_2D, PANAS_diff_neg_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData_PANAS_diff_pos_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(PANAS_diff_pos)$statistic,
`p-value` = shapiro.test(PANAS_diff_pos)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.947 0.319
## 2 1 2 0.974 0.765
## 3 2 1 0.935 0.124
## 4 2 2 0.962 0.606myData_PANAS_diff_neg_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(PANAS_diff_neg)$statistic,
`p-value` = shapiro.test(PANAS_diff_neg)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.937 0.236
## 2 1 2 0.971 0.721
## 3 2 1 0.913 0.0468
## 4 2 2 0.910 0.0627#Perform QQ plots by group
ggplot(data = myData_PANAS_diff_pos_noOutliers, mapping = aes(sample = PANAS_diff_pos, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_PANAS_diff_neg_noOutliers, mapping = aes(sample = PANAS_diff_pos, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_PANAS_diff_pos <- aov(PANAS_diff_pos ~ Task_2DvAR * RvS, data = myData_PANAS_diff_pos_noOutliers)
summary(ANOVA_PANAS_diff_pos)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.143 0.1428 0.388 0.535
## RvS 1 0.000 0.0001 0.000 0.987
## Task_2DvAR:RvS 1 0.171 0.1711 0.465 0.497
## Residuals 83 30.525 0.3678
## 2 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_PANAS_diff_pos)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.2011 0.8954
## 83ANOVA_PANAS_diff_neg <- aov(PANAS_diff_neg ~ Task_2DvAR * RvS, data = myData_PANAS_diff_neg_noOutliers)
summary(ANOVA_PANAS_diff_neg)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.107 0.10723 2.663 0.1066
## RvS 1 0.236 0.23649 5.873 0.0176 *
## Task_2DvAR:RvS 1 0.005 0.00474 0.118 0.7325
## Residuals 81 3.261 0.04026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_PANAS_diff_neg)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 3.4436 0.02051 *
## 81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1effectsize::eta_squared(ANOVA_PANAS_diff_neg, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.03 | [0.00, 1.00]
## RvS | 0.07 | [0.01, 1.00]
## Task_2DvAR:RvS | 1.45e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].Descriptive statistic per group
# Positive affect
myData_PANAS_diff_pos_noOutliers %>% select(PANAS_diff_pos, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(PANAS_diff_pos, na.rm = TRUE),
sd = sd(PANAS_diff_pos, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(PANAS_diff_pos, na.rm = TRUE),
min=min(PANAS_diff_pos, na.rm = TRUE),
max=max(PANAS_diff_pos, na.rm = TRUE),
IQR=IQR(PANAS_diff_pos, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 0.065 0.591 0.132 -0.212 0.342 0.1 -1.3 1
## 2 1 2 24 -0.025 0.652 0.133 -0.300 0.250 0 -1.4 1.2
## 3 2 1 25 -0.104 0.597 0.119 -0.350 0.142 0 -1.3 0.8
## 4 2 2 20 -0.0158 0.574 0.128 -0.284 0.253 0.1 -1.2 0.9
## # ℹ 1 more variable: IQR <dbl># Negative affect
myData_PANAS_diff_neg_noOutliers %>% select(PANAS_diff_neg, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(PANAS_diff_neg, na.rm = TRUE),
sd = sd(PANAS_diff_neg, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(PANAS_diff_neg, na.rm = TRUE),
min=min(PANAS_diff_neg, na.rm = TRUE),
max=max(PANAS_diff_neg, na.rm = TRUE),
IQR=IQR(PANAS_diff_neg, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 0.0211 0.196 0.0438 -0.0707 0.113 0 -0.3 0.4
## 2 1 2 24 -0.1 0.278 0.0567 -0.217 0.0174 -0.1 -0.7 0.4
## 3 2 1 25 -0.0739 0.132 0.0264 -0.128 -0.0194 0 -0.4 0.2
## 4 2 2 20 -0.165 0.160 0.0357 -0.240 -0.0902 -0.15 -0.4 0.1
## # ℹ 1 more variable: IQR <dbl>SSQ - Cybersickness
Checking for outliers using Box-Plots
plot_SSQ_diff_N <- ggplot(myData, aes(factor(Task_2DvAR), SSQ_diff_N, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference Nausea")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_SSQ_diff_O <- ggplot(myData, aes(factor(Task_2DvAR), SSQ_diff_O, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference Oc. Disturbance")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_SSQ_diff_D <- ggplot(myData, aes(factor(Task_2DvAR), SSQ_diff_D, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference Disorientation")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_SSQ_diff_TS <- ggplot(myData, aes(factor(Task_2DvAR), SSQ_diff_TS, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Difference Total Score")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
ggarrange(plot_SSQ_diff_N, plot_SSQ_diff_O, plot_SSQ_diff_D, plot_SSQ_diff_TS,
#labels = c("Nausea", "Oculomotor Disturbance", "Disorientation", "Total Score"),
ncol = 2, nrow = 2)
Removing outliers when Interquartile Range IQR > 1.5
SSQ_diff_N_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_N_2D <- boxplot(SSQ_diff_N_2D$SSQ_diff_N, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_N_2D[SSQ_diff_N_2D$SSQ_diff_N %in% outliers_SSQ_diff_N_2D, "SSQ_diff_N"] = NA
SSQ_diff_N_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_N_AR <- boxplot(SSQ_diff_N_AR$SSQ_diff_N, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_N_AR[SSQ_diff_N_AR$SSQ_diff_N %in% outliers_SSQ_diff_N_AR, "SSQ_diff_N"] = NA
myData_SSQ_diff_N_noOutliers <- rbind(SSQ_diff_N_2D, SSQ_diff_N_AR)
SSQ_diff_O_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_O_2D <- boxplot(SSQ_diff_O_2D$SSQ_diff_O, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_O_2D[SSQ_diff_O_2D$SSQ_diff_O %in% outliers_SSQ_diff_O_2D, "SSQ_diff_O"] = NA
SSQ_diff_O_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_O_AR <- boxplot(SSQ_diff_O_AR$SSQ_diff_O, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_O_AR[SSQ_diff_O_AR$SSQ_diff_O %in% outliers_SSQ_diff_O_AR, "SSQ_diff_O"] = NA
myData_SSQ_diff_O_noOutliers <- rbind(SSQ_diff_O_2D, SSQ_diff_O_AR)
SSQ_diff_D_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_D_2D <- boxplot(SSQ_diff_D_2D$SSQ_diff_D, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_D_2D[SSQ_diff_D_2D$SSQ_diff_D %in% outliers_SSQ_diff_D_2D, "SSQ_diff_D"] = NA
SSQ_diff_D_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_D_AR <- boxplot(SSQ_diff_D_AR$SSQ_diff_D, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_D_AR[SSQ_diff_D_AR$SSQ_diff_D %in% outliers_SSQ_diff_D_AR, "SSQ_diff_D"] = NA
myData_SSQ_diff_D_noOutliers <- rbind(SSQ_diff_D_2D, SSQ_diff_D_AR)
SSQ_diff_TS_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_TS_2D <- boxplot(SSQ_diff_TS_2D$SSQ_diff_TS, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_TS_2D[SSQ_diff_TS_2D$SSQ_diff_TS %in% outliers_SSQ_diff_TS_2D, "SSQ_diff_TS"] = NA
SSQ_diff_TS_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_SSQ_diff_TS_AR <- boxplot(SSQ_diff_TS_AR$SSQ_diff_TS, plot = FALSE)$out
# Replace the values with NA
SSQ_diff_TS_AR[SSQ_diff_TS_AR$SSQ_diff_TS %in% outliers_SSQ_diff_TS_AR, "SSQ_diff_TS"] = NA
myData_SSQ_diff_TS_noOutliers <- rbind(SSQ_diff_TS_2D, SSQ_diff_TS_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData_SSQ_diff_N_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(SSQ_diff_N)$statistic,
`p-value` = shapiro.test(SSQ_diff_N)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.879 0.0202
## 2 1 2 0.877 0.00889
## 3 2 1 0.864 0.00332
## 4 2 2 0.921 0.116myData_SSQ_diff_O_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(SSQ_diff_O)$statistic,
`p-value` = shapiro.test(SSQ_diff_O)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.972 0.820
## 2 1 2 0.959 0.418
## 3 2 1 0.956 0.372
## 4 2 2 0.937 0.236myData_SSQ_diff_D_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(SSQ_diff_D)$statistic,
`p-value` = shapiro.test(SSQ_diff_D)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.861 0.0103
## 2 1 2 0.846 0.00235
## 3 2 1 0.879 0.0115
## 4 2 2 0.826 0.00358myData_SSQ_diff_TS_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(SSQ_diff_TS)$statistic,
`p-value` = shapiro.test(SSQ_diff_TS)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.935 0.214
## 2 1 2 0.900 0.0257
## 3 2 1 0.968 0.699
## 4 2 2 0.860 0.0389#Perform QQ plots by group
ggplot(data = myData_SSQ_diff_N_noOutliers, mapping = aes(sample = SSQ_diff_N, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_SSQ_diff_O_noOutliers, mapping = aes(sample = SSQ_diff_O, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_SSQ_diff_D_noOutliers, mapping = aes(sample = SSQ_diff_D, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_SSQ_diff_TS_noOutliers, mapping = aes(sample = SSQ_diff_TS, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_SSQ_diff_N <- aov(SSQ_diff_N ~ Task_2DvAR *RvS, data = myData_SSQ_diff_N_noOutliers)
summary(ANOVA_SSQ_diff_N)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.09 0.094 0.046 0.8315
## RvS 1 6.64 6.641 3.215 0.0766 .
## Task_2DvAR:RvS 1 7.54 7.535 3.648 0.0596 .
## Residuals 82 169.37 2.066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_SSQ_diff_N)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.8715 0.1408
## 82TukeyHSD(ANOVA_SSQ_diff_N)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SSQ_diff_N ~ Task_2DvAR * RvS, data = myData_SSQ_diff_N_noOutliers)
##
## $Task_2DvAR
## diff lwr upr p adj
## 2-1 -0.0661812 -0.6829452 0.5505828 0.8314967
##
## $RvS
## diff lwr upr p adj
## 2-1 -0.5521784 -1.168942 0.06458558 0.0786151
##
## $`Task_2DvAR:RvS`
## diff lwr upr p adj
## 2:1-1:1 0.45430376 -0.6928257 1.601433184 0.7273438
## 1:2-1:1 0.04759072 -1.1208773 1.216058688 0.9995603
## 2:2-1:1 -0.69067669 -1.9135208 0.532167383 0.4534149
## 1:2-2:1 -0.40671304 -1.4956922 0.682266151 0.7615055
## 2:2-2:1 -1.14498045 -2.2921099 0.002148973 0.0506143
## 2:2-1:2 -0.73826741 -1.9067354 0.430200565 0.3530443effectsize::eta_squared(ANOVA_SSQ_diff_N, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 5.55e-04 | [0.00, 1.00]
## RvS | 0.04 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_SSQ_diff_O <- aov(SSQ_diff_O ~ Task_2DvAR *RvS, data = myData_SSQ_diff_O_noOutliers)
summary(ANOVA_SSQ_diff_O)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.0 0.014 0.002 0.964
## RvS 1 2.4 2.378 0.350 0.556
## Task_2DvAR:RvS 1 4.2 4.227 0.622 0.433
## Residuals 82 557.4 6.798
## 3 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_SSQ_diff_O)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.2815 0.8386
## 82ANOVA_SSQ_diff_D <- aov(SSQ_diff_D ~ Task_2DvAR *RvS, data = myData_SSQ_diff_D_noOutliers)
summary(ANOVA_SSQ_diff_D)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 1.6 1.599 0.330 0.568
## RvS 1 4.5 4.452 0.918 0.341
## Task_2DvAR:RvS 1 0.3 0.252 0.052 0.820
## Residuals 78 378.4 4.851
## 7 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_SSQ_diff_D)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.7093 0.1719
## 78ANOVA_SSQ_diff_TS <- aov(SSQ_diff_TS ~ Task_2DvAR *RvS, data = myData_SSQ_diff_TS_noOutliers)
summary(ANOVA_SSQ_diff_TS)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.000 0.00039 0.001 0.970
## RvS 1 0.067 0.06674 0.247 0.620
## Task_2DvAR:RvS 1 0.022 0.02181 0.081 0.777
## Residuals 72 19.423 0.26976
## 13 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_SSQ_diff_TS)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 3.0898 0.03238 *
## 72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Descriptive statistic per group
# Nausea
myData_SSQ_diff_N_noOutliers %>% select(SSQ_diff_N, RvS) %>% group_by(RvS) %>%
summarise(n = n(),
mean = mean(SSQ_diff_N, na.rm = TRUE),
sd = sd(SSQ_diff_N, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(SSQ_diff_N, na.rm = TRUE),
min=min(SSQ_diff_N, na.rm = TRUE),
max=max(SSQ_diff_N, na.rm = TRUE),
IQR=IQR(SSQ_diff_N, na.rm = TRUE))## # A tibble: 2 × 11
## RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 45 -0.0377 1.24 0.185 -0.411 0.336 -0.00571 -4.02 1.57 1.73
## 2 2 44 -0.582 1.64 0.248 -1.08 -0.0822 -0.22 -4.45 1.43 2.58# Oculomotor Disturbance
myData_SSQ_diff_O_noOutliers %>% select(SSQ_diff_O, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(SSQ_diff_O, na.rm = TRUE),
sd = sd(SSQ_diff_O, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(SSQ_diff_O, na.rm = TRUE),
min=min(SSQ_diff_O, na.rm = TRUE),
max=max(SSQ_diff_O, na.rm = TRUE),
IQR=IQR(SSQ_diff_O, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 1.20 2.65 0.593 -0.0433 2.44 1.08 -3.25 6.50
## 2 1 2 24 1.31 2.26 0.461 0.355 2.26 1.08 -3.25 5.41
## 3 2 1 25 1.58 2.63 0.527 0.492 2.67 1.08 -4.33 7.58
## 4 2 2 20 0.798 2.93 0.655 -0.573 2.17 1.08 -4.33 7.58
## # ℹ 1 more variable: IQR <dbl># Disorientation
myData_SSQ_diff_D_noOutliers %>% select(SSQ_diff_D, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(SSQ_diff_D, na.rm = TRUE),
sd = sd(SSQ_diff_D, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(SSQ_diff_D, na.rm = TRUE),
min=min(SSQ_diff_D, na.rm = TRUE),
max=max(SSQ_diff_D, na.rm = TRUE),
IQR=IQR(SSQ_diff_D, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 1.57 3.15 0.704 0.0958 3.04 0 -1.99 7.95
## 2 1 2 24 1.21 1.77 0.362 0.462 1.96 0 -1.99 5.97
## 3 2 1 25 1.36 1.88 0.376 0.580 2.13 1.99 -1.99 3.98
## 4 2 2 20 0.773 1.82 0.408 -0.0796 1.63 0 -1.99 3.98
## # ℹ 1 more variable: IQR <dbl># Total Score
myData_SSQ_diff_TS_noOutliers %>% select(SSQ_diff_TS, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(SSQ_diff_TS, na.rm = TRUE),
sd = sd(SSQ_diff_TS, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(SSQ_diff_TS, na.rm = TRUE),
min=min(SSQ_diff_TS, na.rm = TRUE),
max=max(SSQ_diff_TS, na.rm = TRUE),
IQR=IQR(SSQ_diff_TS, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 0.295 0.683 0.153 -0.0244 0.615 0.234 -0.701 1.40
## 2 1 2 24 0.325 0.502 0.102 0.113 0.537 0.468 -0.701 1.17
## 3 2 1 25 0.278 0.447 0.0895 0.0936 0.463 0.234 -0.701 1.17
## 4 2 2 20 0.378 0.351 0.0785 0.213 0.542 0.234 0 1.17
## # ℹ 1 more variable: IQR <dbl>FSK - Flow
Checking for outliers using Box-Plots
plot_Flow_Glatter_Verlauf <- ggplot(myData, aes(factor(Task_2DvAR), Flow_Glatter_Verlauf, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Fluency")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Flow_Absorbiertheit <- ggplot(myData, aes(factor(Task_2DvAR), Flow_Absorbiertheit, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Absorption")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Flow_Besorgnis <- ggplot(myData, aes(factor(Task_2DvAR), Flow_Besorgnis, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Concern")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Flow_Total <- ggplot(myData, aes(factor(Task_2DvAR), Flow_Total, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "General factor")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
ggarrange(plot_Flow_Glatter_Verlauf, plot_Flow_Absorbiertheit, plot_Flow_Besorgnis, plot_Flow_Total,
# labels = c("Fluency", "Absorption", "Concern", "General factor"),
ncol = 2, nrow = 2)
Removing outliers when Interquartile Range IQR > 1.5
Flow_Glatter_Verlauf_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Glatter_Verlauf_2D <- boxplot(Flow_Glatter_Verlauf_2D$Flow_Glatter_Verlauf, plot = FALSE)$out
# Replace the values with NA
Flow_Glatter_Verlauf_2D[Flow_Glatter_Verlauf_2D$Flow_Glatter_Verlauf %in% outliers_Flow_Glatter_Verlauf_2D, "Flow_Glatter_Verlauf"] = NA
Flow_Glatter_Verlauf_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Glatter_Verlauf_AR <- boxplot(Flow_Glatter_Verlauf_AR$Flow_Glatter_Verlauf, plot = FALSE)$out
# Replace the values with NA
Flow_Glatter_Verlauf_AR[Flow_Glatter_Verlauf_AR$Flow_Glatter_Verlauf %in% outliers_Flow_Glatter_Verlauf_AR, "Flow_Glatter_Verlauf"] = NA
myData_Flow_Glatter_Verlauf_noOutliers <- rbind(Flow_Glatter_Verlauf_2D, Flow_Glatter_Verlauf_AR)
Flow_Absorbiertheit_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Absorbiertheit_2D <- boxplot(Flow_Absorbiertheit_2D$Flow_Absorbiertheit, plot = FALSE)$out
# Replace the values with NA
Flow_Absorbiertheit_2D[Flow_Absorbiertheit_2D$Flow_Absorbiertheit %in% outliers_Flow_Absorbiertheit_2D, "Flow_Absorbiertheit"] = NA
Flow_Absorbiertheit_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Absorbiertheit_AR <- boxplot(Flow_Absorbiertheit_AR$Flow_Absorbiertheit, plot = FALSE)$out
# Replace the values with NA
Flow_Absorbiertheit_AR[Flow_Absorbiertheit_AR$Flow_Absorbiertheit %in% outliers_Flow_Absorbiertheit_AR, "Flow_Absorbiertheit"] = NA
myData_Flow_Absorbiertheit_noOutliers <- rbind(Flow_Absorbiertheit_2D, Flow_Absorbiertheit_AR)
Flow_Besorgnis_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Besorgnis_2D <- boxplot(Flow_Besorgnis_2D$Flow_Besorgnis, plot = FALSE)$out
# Replace the values with NA
Flow_Besorgnis_2D[Flow_Besorgnis_2D$Flow_Besorgnis %in% outliers_Flow_Besorgnis_2D, "Flow_Besorgnis"] = NA
Flow_Besorgnis_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Besorgnis_AR <- boxplot(Flow_Besorgnis_AR$Flow_Besorgnis, plot = FALSE)$out
# Replace the values with NA
Flow_Besorgnis_AR[Flow_Besorgnis_AR$Flow_Besorgnis %in% outliers_Flow_Besorgnis_AR, "Flow_Besorgnis"] = NA
myData_Flow_Besorgnis_noOutliers <- rbind(Flow_Besorgnis_2D, Flow_Besorgnis_AR)
Flow_Total_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Total_2D <- boxplot(Flow_Total_2D$Flow_Total, plot = FALSE)$out
# Replace the values with NA
Flow_Total_2D[Flow_Total_2D$Flow_Total %in% outliers_Flow_Total_2D, "Flow_Total"] = NA
Flow_Total_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Flow_Total_AR <- boxplot(Flow_Total_AR$Flow_Total, plot = FALSE)$out
# Replace the values with NA
Flow_Total_AR[Flow_Total_AR$Flow_Total %in% outliers_Flow_Total_AR, "Flow_Total"] = NA
myData_Flow_Total_noOutliers <- rbind(Flow_Total_2D, Flow_Total_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData_Flow_Glatter_Verlauf_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Flow_Glatter_Verlauf)$statistic,
`p-value` = shapiro.test(Flow_Glatter_Verlauf)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.991 0.999
## 2 1 2 0.968 0.606
## 3 2 1 0.968 0.604
## 4 2 2 0.983 0.965myData_Flow_Absorbiertheit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Flow_Absorbiertheit)$statistic,
`p-value` = shapiro.test(Flow_Absorbiertheit)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.959 0.533
## 2 1 2 0.939 0.152
## 3 2 1 0.913 0.0474
## 4 2 2 0.938 0.221myData_Flow_Besorgnis_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Flow_Besorgnis)$statistic,
`p-value` = shapiro.test(Flow_Besorgnis)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.855 0.00648
## 2 1 2 0.917 0.0490
## 3 2 1 0.893 0.0155
## 4 2 2 0.881 0.0276myData_Flow_Total_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Flow_Total)$statistic,
`p-value` = shapiro.test(Flow_Total)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.938 0.217
## 2 1 2 0.969 0.654
## 3 2 1 0.982 0.933
## 4 2 2 0.971 0.782#Perform QQ plots by group
ggplot(data = myData_Flow_Glatter_Verlauf_noOutliers, mapping = aes(sample = Flow_Glatter_Verlauf, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Flow_Absorbiertheit_noOutliers, mapping = aes(sample = Flow_Absorbiertheit, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Flow_Besorgnis_noOutliers, mapping = aes(sample = Flow_Besorgnis, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Flow_Total_noOutliers, mapping = aes(sample = Flow_Total, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_Flow_Glatter_Verlauf <- aov(Flow_Glatter_Verlauf ~ Task_2DvAR *RvS, data = myData_Flow_Glatter_Verlauf_noOutliers)
summary(ANOVA_Flow_Glatter_Verlauf)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 4.95 4.949 3.369 0.0699 .
## RvS 1 1.17 1.166 0.794 0.3754
## Task_2DvAR:RvS 1 5.79 5.793 3.944 0.0503 .
## Residuals 85 124.85 1.469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(ANOVA_Flow_Glatter_Verlauf)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3547 0.7858
## 85TukeyHSD(ANOVA_Flow_Glatter_Verlauf)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Flow_Glatter_Verlauf ~ Task_2DvAR * RvS, data = myData_Flow_Glatter_Verlauf_noOutliers)
##
## $Task_2DvAR
## diff lwr upr p adj
## 2-1 0.471633 -0.03925164 0.9825176 0.0699276
##
## $RvS
## diff lwr upr p adj
## 2-1 0.2277744 -0.2831103 0.738659 0.3778733
##
## $`Task_2DvAR:RvS`
## diff lwr upr p adj
## 2:1-1:1 -0.01333333 -0.96614274 0.9394761 0.9999821
## 1:2-1:1 -0.28750000 -1.24909125 0.6740912 0.8617388
## 2:2-1:1 0.72500000 -0.27934930 1.7293493 0.2393234
## 1:2-2:1 -0.27416667 -1.18179323 0.6334599 0.8580758
## 2:2-2:1 0.73833333 -0.21447607 1.6911427 0.1849493
## 2:2-1:2 1.01250000 0.05090875 1.9740912 0.0350802effectsize::eta_squared(ANOVA_Flow_Glatter_Verlauf, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.04 | [0.00, 1.00]
## RvS | 9.25e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_Flow_Absorbiertheit <- aov(Flow_Absorbiertheit ~ Task_2DvAR *RvS, data = myData_Flow_Absorbiertheit_noOutliers)
summary(ANOVA_Flow_Absorbiertheit)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 2.54 2.5392 2.123 0.149
## RvS 1 0.39 0.3935 0.329 0.568
## Task_2DvAR:RvS 1 0.00 0.0022 0.002 0.966
## Residuals 83 99.28 1.1962
## 2 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Flow_Absorbiertheit)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.6474 0.1847
## 83ANOVA_Flow_Besorgnis <- aov(Flow_Besorgnis ~ Task_2DvAR *RvS, data = myData_Flow_Besorgnis_noOutliers)
summary(ANOVA_Flow_Besorgnis)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 6.33 6.326 4.243 0.0426 *
## RvS 1 3.00 3.003 2.014 0.1596
## Task_2DvAR:RvS 1 0.19 0.194 0.130 0.7189
## Residuals 82 122.25 1.491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Flow_Besorgnis)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.5103 0.218
## 82effectsize::eta_squared(ANOVA_Flow_Besorgnis, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.05 | [0.00, 1.00]
## RvS | 0.02 | [0.00, 1.00]
## Task_2DvAR:RvS | 1.59e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_Flow_Total <- aov(Flow_Total ~ Task_2DvAR *RvS, data = myData_Flow_Total_noOutliers)
summary(ANOVA_Flow_Total)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 4.10 4.102 3.949 0.0501 .
## RvS 1 0.72 0.720 0.693 0.4074
## Task_2DvAR:RvS 1 2.02 2.019 1.943 0.1670
## Residuals 84 87.25 1.039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 Beobachtung als fehlend gelöschtleveneTest(ANOVA_Flow_Total)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.2762 0.8424
## 84effectsize::eta_squared(ANOVA_Flow_Total, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.04 | [0.00, 1.00]
## RvS | 8.19e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.02 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].Descriptive statistic per group
# Fluency
myData_Flow_Glatter_Verlauf_noOutliers %>% select(Flow_Glatter_Verlauf, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Flow_Glatter_Verlauf, na.rm = TRUE),
sd = sd(Flow_Glatter_Verlauf, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Flow_Glatter_Verlauf, na.rm = TRUE),
min=min(Flow_Glatter_Verlauf, na.rm = TRUE),
max=max(Flow_Glatter_Verlauf, na.rm = TRUE),
IQR=IQR(Flow_Glatter_Verlauf, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.93 1.06 0.238 3.44 4.43 3.92 1.67 6 1.25
## 2 1 2 24 3.65 1.18 0.241 3.15 4.14 3.42 1.5 5.67 1.83
## 3 2 1 25 3.92 1.35 0.270 3.36 4.48 4.17 1.5 6.83 1.83
## 4 2 2 20 4.66 1.21 0.270 4.09 5.22 4.58 2 6.67 1.54# Absorption
myData_Flow_Absorbiertheit_noOutliers %>% select(Flow_Absorbiertheit, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Flow_Absorbiertheit, na.rm = TRUE),
sd = sd(Flow_Absorbiertheit, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Flow_Absorbiertheit, na.rm = TRUE),
min=min(Flow_Absorbiertheit, na.rm = TRUE),
max=max(Flow_Absorbiertheit, na.rm = TRUE),
IQR=IQR(Flow_Absorbiertheit, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.12 1.37 0.307 3.48 4.77 4 2 6.75 2.06
## 2 1 2 24 4.25 0.953 0.194 3.85 4.65 4 3 6.5 1.31
## 3 2 1 25 4.47 0.981 0.196 4.06 4.87 4.25 2.5 6.5 1.25
## 4 2 2 20 4.61 1.06 0.238 4.12 5.11 4.75 2.25 6 1.56# Concern
myData_Flow_Besorgnis_noOutliers %>% select(Flow_Besorgnis, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Flow_Besorgnis, na.rm = TRUE),
sd = sd(Flow_Besorgnis, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Flow_Besorgnis, na.rm = TRUE),
min=min(Flow_Besorgnis, na.rm = TRUE),
max=max(Flow_Besorgnis, na.rm = TRUE),
IQR=IQR(Flow_Besorgnis, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 2.35 1.35 0.302 1.72 2.98 1.83 1 5.33 2
## 2 1 2 24 2.82 1.41 0.289 2.22 3.42 2.67 1 5.33 2.67
## 3 2 1 25 1.94 0.866 0.173 1.59 2.30 1.67 1 3.67 1.42
## 4 2 2 20 2.22 1.20 0.268 1.66 2.78 2.17 1 4.67 2.00# General Factor
myData_Flow_Total_noOutliers %>% select(Flow_Total, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Flow_Total, na.rm = TRUE),
sd = sd(Flow_Total, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Flow_Total, na.rm = TRUE),
min=min(Flow_Total, na.rm = TRUE),
max=max(Flow_Total, na.rm = TRUE),
IQR=IQR(Flow_Total, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.01 1.02 0.228 3.53 4.49 4.2 2.2 5.6 1.50
## 2 1 2 24 3.89 0.922 0.188 3.50 4.28 3.85 2.3 6 1.45
## 3 2 1 25 4.15 1.12 0.224 3.69 4.62 4.2 1.9 6.6 1.08
## 4 2 2 20 4.64 1.01 0.225 4.17 5.11 4.75 2.7 6.3 1.17TUI
Checking for outliers using Box-Plots
plot_TUI_Neugierde <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Neugierde, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Curiosity")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Technologieaengstlichkeit <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Technologieaengstlichkeit, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Tech. Fear")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Interesse <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Interesse, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Interest")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Benutzerfreundlichkeit <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Benutzerfreundlichkeit, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Usability")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Immersion <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Immersion, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Immersion")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Nuetzlichkeit <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Nuetzlichkeit, )) + geom_boxplot() + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Usefulness")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Skepsis <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Skepsis, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Skepticism")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_TUI_Zugaenglichkeit <- ggplot(myData, aes(factor(Task_2DvAR), TUI_Zugaenglichkeit, )) + geom_boxplot() + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Accessibility")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
ggarrange(plot_TUI_Neugierde, plot_TUI_Technologieaengstlichkeit, plot_TUI_Interesse, plot_TUI_Benutzerfreundlichkeit, plot_TUI_Immersion, plot_TUI_Nuetzlichkeit, plot_TUI_Skepsis, plot_TUI_Zugaenglichkeit,
#labels = c("Curiosity", "Technology Fear", "Interest", "Usability", "Immersion", "Usefulness", #"Skepticism", "Accessibility"),
ncol = 2, nrow = 4)
Removing outliers when Interquartile Range IQR > 1.5
TUI_Technologieaengstlichkeit_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Technologieaengstlichkeit_2D <- boxplot(TUI_Technologieaengstlichkeit_2D$TUI_Technologieaengstlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Technologieaengstlichkeit_2D[TUI_Technologieaengstlichkeit_2D$TUI_Technologieaengstlichkeit %in% outliers_TUI_Technologieaengstlichkeit_2D, "TUI_Technologieaengstlichkeit"] = NA
TUI_Technologieaengstlichkeit_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Technologieaengstlichkeit_AR <- boxplot(TUI_Technologieaengstlichkeit_AR$TUI_Technologieaengstlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Technologieaengstlichkeit_AR[TUI_Technologieaengstlichkeit_AR$TUI_Technologieaengstlichkeit %in% outliers_TUI_Technologieaengstlichkeit_AR, "TUI_Technologieaengstlichkeit"] = NA
myData_TUI_Technologieaengstlichkeit_noOutliers <- rbind(TUI_Technologieaengstlichkeit_2D, TUI_Technologieaengstlichkeit_AR)
TUI_Benutzerfreundlichkeit_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Benutzerfreundlichkeit_2D <- boxplot(TUI_Benutzerfreundlichkeit_2D$TUI_Benutzerfreundlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Benutzerfreundlichkeit_2D[TUI_Benutzerfreundlichkeit_2D$TUI_Benutzerfreundlichkeit %in% outliers_TUI_Benutzerfreundlichkeit_2D, "TUI_Benutzerfreundlichkeit"] = NA
TUI_Benutzerfreundlichkeit_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Benutzerfreundlichkeit_AR <- boxplot(TUI_Benutzerfreundlichkeit_AR$TUI_Benutzerfreundlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Benutzerfreundlichkeit_AR[TUI_Benutzerfreundlichkeit_AR$TUI_Benutzerfreundlichkeit %in% outliers_TUI_Benutzerfreundlichkeit_AR, "TUI_Benutzerfreundlichkeit"] = NA
myData_TUI_Benutzerfreundlichkeit_noOutliers <- rbind(TUI_Benutzerfreundlichkeit_2D, TUI_Benutzerfreundlichkeit_AR)
TUI_Nuetzlichkeit_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Nuetzlichkeit_2D <- boxplot(TUI_Nuetzlichkeit_2D$TUI_Nuetzlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Nuetzlichkeit_2D[TUI_Nuetzlichkeit_2D$TUI_Nuetzlichkeit %in% outliers_TUI_Nuetzlichkeit_2D, "TUI_Nuetzlichkeit"] = NA
TUI_Nuetzlichkeit_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Nuetzlichkeit_AR <- boxplot(TUI_Nuetzlichkeit_AR$TUI_Nuetzlichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Nuetzlichkeit_AR[TUI_Nuetzlichkeit_AR$TUI_Nuetzlichkeit %in% outliers_TUI_Nuetzlichkeit_AR, "TUI_Nuetzlichkeit"] = NA
myData_TUI_Nuetzlichkeit_noOutliers <- rbind(TUI_Nuetzlichkeit_2D, TUI_Nuetzlichkeit_AR)
TUI_Zugaenglichkeit_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Zugaenglichkeit_2D <- boxplot(TUI_Zugaenglichkeit_2D$TUI_Zugaenglichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Zugaenglichkeit_2D[TUI_Zugaenglichkeit_2D$TUI_Zugaenglichkeit %in% outliers_TUI_Zugaenglichkeit_2D, "TUI_Zugaenglichkeit"] = NA
TUI_Zugaenglichkeit_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_TUI_Zugaenglichkeit_AR <- boxplot(TUI_Zugaenglichkeit_AR$TUI_Zugaenglichkeit, plot = FALSE)$out
# Replace the values with NA
TUI_Zugaenglichkeit_AR[TUI_Zugaenglichkeit_AR$TUI_Zugaenglichkeit %in% outliers_TUI_Zugaenglichkeit_AR, "TUI_Zugaenglichkeit"] = NA
myData_TUI_Zugaenglichkeit_noOutliers <- rbind(TUI_Zugaenglichkeit_2D, TUI_Zugaenglichkeit_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Neugierde)$statistic,
`p-value` = shapiro.test(TUI_Neugierde)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.981 0.950
## 2 1 2 0.947 0.233
## 3 2 1 0.941 0.159
## 4 2 2 0.950 0.374myData_TUI_Technologieaengstlichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Technologieaengstlichkeit)$statistic,
`p-value` = shapiro.test(TUI_Technologieaengstlichkeit)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.929 0.150
## 2 1 2 0.924 0.0713
## 3 2 1 0.773 0.000110
## 4 2 2 0.843 0.00405myData %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Interesse)$statistic,
`p-value` = shapiro.test(TUI_Interesse)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.889 0.0261
## 2 1 2 0.950 0.266
## 3 2 1 0.928 0.0774
## 4 2 2 0.911 0.0663myData_TUI_Benutzerfreundlichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Benutzerfreundlichkeit)$statistic,
`p-value` = shapiro.test(TUI_Benutzerfreundlichkeit)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.844 0.00422
## 2 1 2 0.937 0.136
## 3 2 1 0.917 0.0571
## 4 2 2 0.944 0.279myData %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Immersion)$statistic,
`p-value` = shapiro.test(TUI_Immersion)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.950 0.374
## 2 1 2 0.940 0.166
## 3 2 1 0.960 0.422
## 4 2 2 0.986 0.987myData_TUI_Nuetzlichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Nuetzlichkeit)$statistic,
`p-value` = shapiro.test(TUI_Nuetzlichkeit)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.940 0.238
## 2 1 2 0.819 0.000624
## 3 2 1 0.943 0.191
## 4 2 2 0.904 0.0500myData %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Skepsis)$statistic,
`p-value` = shapiro.test(TUI_Skepsis)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.954 0.427
## 2 1 2 0.921 0.0609
## 3 2 1 0.927 0.0723
## 4 2 2 0.917 0.0875myData_TUI_Zugaenglichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(TUI_Zugaenglichkeit)$statistic,
`p-value` = shapiro.test(TUI_Zugaenglichkeit)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.948 0.336
## 2 1 2 0.954 0.337
## 3 2 1 0.846 0.00182
## 4 2 2 0.903 0.0550#Perform QQ plots by group
ggplot(data = myData, mapping = aes(sample = TUI_Neugierde, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_TUI_Technologieaengstlichkeit_noOutliers, mapping = aes(sample = TUI_Technologieaengstlichkeit, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData, mapping = aes(sample = TUI_Interesse, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_TUI_Benutzerfreundlichkeit_noOutliers, mapping = aes(sample = TUI_Benutzerfreundlichkeit, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData, mapping = aes(sample = TUI_Immersion, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_TUI_Nuetzlichkeit_noOutliers, mapping = aes(sample = TUI_Nuetzlichkeit, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData, mapping = aes(sample = TUI_Skepsis, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_TUI_Zugaenglichkeit_noOutliers, mapping = aes(sample = TUI_Zugaenglichkeit, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_TUI_Neugierde <- aov(TUI_Neugierde ~ Task_2DvAR *RvS, data = myData)
summary(ANOVA_TUI_Neugierde)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 3.13 3.1306 1.841 0.178
## RvS 1 0.48 0.4789 0.282 0.597
## Task_2DvAR:RvS 1 0.19 0.1918 0.113 0.738
## Residuals 85 144.58 1.7009leveneTest(ANOVA_TUI_Neugierde)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.1015 0.959
## 85ANOVA_TUI_Technologieaengstlichkeit <- aov(TUI_Technologieaengstlichkeit ~ Task_2DvAR *RvS, data = myData_TUI_Technologieaengstlichkeit_noOutliers)
summary(ANOVA_TUI_Technologieaengstlichkeit)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 1.78 1.7756 3.775 0.0554 .
## RvS 1 0.00 0.0046 0.010 0.9211
## Task_2DvAR:RvS 1 0.95 0.9470 2.013 0.1596
## Residuals 84 39.51 0.4704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 Beobachtung als fehlend gelöschtleveneTest(ANOVA_TUI_Technologieaengstlichkeit)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.0966 0.3552
## 84effectsize::eta_squared(ANOVA_TUI_Technologieaengstlichkeit, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.04 | [0.00, 1.00]
## RvS | 1.17e-04 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.02 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_TUI_Interesse <- aov(TUI_Interesse ~ Task_2DvAR *RvS, data = myData)
summary(ANOVA_TUI_Interesse)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 3.12 3.1201 1.469 0.229
## RvS 1 0.00 0.0031 0.001 0.970
## Task_2DvAR:RvS 1 0.67 0.6727 0.317 0.575
## Residuals 85 180.59 2.1246leveneTest(ANOVA_TUI_Interesse)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.0326 0.3824
## 85ANOVA_TUI_Benutzerfreundlichkeit <- aov(TUI_Benutzerfreundlichkeit ~ Task_2DvAR *RvS, data = myData_TUI_Benutzerfreundlichkeit_noOutliers)
summary(ANOVA_TUI_Benutzerfreundlichkeit)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 2.848 2.8479 9.729 0.00249 **
## RvS 1 0.705 0.7054 2.410 0.12437
## Task_2DvAR:RvS 1 0.245 0.2449 0.837 0.36305
## Residuals 83 24.296 0.2927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_TUI_Benutzerfreundlichkeit)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.6645 0.5762
## 83effectsize::eta_squared(ANOVA_TUI_Benutzerfreundlichkeit, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.10 | [0.02, 1.00]
## RvS | 0.03 | [0.00, 1.00]
## Task_2DvAR:RvS | 9.98e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_TUI_Immersion <- aov(TUI_Immersion ~ Task_2DvAR *RvS, data = myData)
summary(ANOVA_TUI_Immersion)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.28 0.2835 0.131 0.718
## RvS 1 0.00 0.0027 0.001 0.972
## Task_2DvAR:RvS 1 1.59 1.5928 0.738 0.393
## Residuals 85 183.45 2.1582leveneTest(ANOVA_TUI_Immersion)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3925 0.7587
## 85ANOVA_TUI_Nuetzlichkeit <- aov(TUI_Nuetzlichkeit ~ Task_2DvAR *RvS, data = myData_TUI_Nuetzlichkeit_noOutliers)
summary(ANOVA_TUI_Nuetzlichkeit)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.68 0.6825 0.391 0.533
## RvS 1 0.84 0.8367 0.480 0.490
## Task_2DvAR:RvS 1 2.05 2.0463 1.173 0.282
## Residuals 84 146.54 1.7445
## 1 Beobachtung als fehlend gelöschtleveneTest(ANOVA_TUI_Nuetzlichkeit)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.4956 0.6863
## 84ANOVA_TUI_Skepsis <- aov(TUI_Skepsis ~ Task_2DvAR *RvS, data = myData)
summary(ANOVA_TUI_Skepsis)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 1.25 1.2509 0.906 0.344
## RvS 1 0.14 0.1379 0.100 0.753
## Task_2DvAR:RvS 1 2.17 2.1675 1.570 0.214
## Residuals 85 117.34 1.3805leveneTest(ANOVA_TUI_Skepsis)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3744 0.7717
## 85ANOVA_TUI_Zugaenglichkeit <- aov(TUI_Zugaenglichkeit ~ Task_2DvAR *RvS, data = myData_TUI_Zugaenglichkeit_noOutliers)
summary(ANOVA_TUI_Zugaenglichkeit)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 7.44 7.440 5.196 0.0252 *
## RvS 1 1.03 1.029 0.719 0.3991
## Task_2DvAR:RvS 1 3.46 3.457 2.414 0.1241
## Residuals 83 118.85 1.432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_TUI_Zugaenglichkeit)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.1423 0.3369
## 83effectsize::eta_squared(ANOVA_TUI_Zugaenglichkeit, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.06 | [0.00, 1.00]
## RvS | 8.58e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].Descriptive statistic per group
# Curiosity
myData %>% select(TUI_Neugierde, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Neugierde, na.rm = TRUE),
sd = sd(TUI_Neugierde, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Neugierde, na.rm = TRUE),
min=min(TUI_Neugierde, na.rm = TRUE),
max=max(TUI_Neugierde, na.rm = TRUE),
IQR=IQR(TUI_Neugierde, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.24 1.20 0.269 3.68 4.80 4.38 2.25 6.75 1.75
## 2 1 2 24 4.48 1.38 0.281 3.90 5.06 4.75 1 6.5 2.06
## 3 2 1 25 4.72 1.37 0.274 4.15 5.29 5 1 7 1.75
## 4 2 2 20 4.78 1.22 0.274 4.20 5.35 4.5 2.75 6.75 1.31# Technology Fear
myData_TUI_Technologieaengstlichkeit_noOutliers %>% select(TUI_Technologieaengstlichkeit, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Technologieaengstlichkeit, na.rm = TRUE),
sd = sd(TUI_Technologieaengstlichkeit, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Technologieaengstlichkeit, na.rm = TRUE),
min=min(TUI_Technologieaengstlichkeit, na.rm = TRUE),
max=max(TUI_Technologieaengstlichkeit, na.rm = TRUE),
IQR=IQR(TUI_Technologieaengstlichkeit, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 2.02 0.739 0.165 1.68 2.37 2.12 1 3.75 1.25
## 2 1 2 24 1.80 0.630 0.129 1.54 2.07 1.75 1 3 1
## 3 2 1 25 1.53 0.591 0.118 1.29 1.78 1.38 1 3.5 0.25
## 4 2 2 20 1.72 0.794 0.178 1.35 2.10 1.25 1 3.75 1.25# Interest
myData %>% select(TUI_Interesse, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Interesse, na.rm = TRUE),
sd = sd(TUI_Interesse, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Interesse, na.rm = TRUE),
min=min(TUI_Interesse, na.rm = TRUE),
max=max(TUI_Interesse, na.rm = TRUE),
IQR=IQR(TUI_Interesse, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.49 1.20 0.269 2.93 4.05 3.75 1.5 5 2
## 2 1 2 24 3.32 1.42 0.290 2.72 3.92 3 1 6.25 2.19
## 3 2 1 25 3.69 1.49 0.298 3.08 4.30 3.75 1.75 6.5 2.25
## 4 2 2 20 3.88 1.68 0.376 3.09 4.66 3.62 1.75 6.75 2.94# Usability
myData_TUI_Benutzerfreundlichkeit_noOutliers %>% select(TUI_Benutzerfreundlichkeit, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Benutzerfreundlichkeit, na.rm = TRUE),
sd = sd(TUI_Benutzerfreundlichkeit, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Benutzerfreundlichkeit, na.rm = TRUE),
min=min(TUI_Benutzerfreundlichkeit, na.rm = TRUE),
max=max(TUI_Benutzerfreundlichkeit, na.rm = TRUE),
IQR=IQR(TUI_Benutzerfreundlichkeit, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.38 0.642 0.144 4.08 4.68 4.33 3 5 0.750
## 2 1 2 24 4.10 0.543 0.111 3.87 4.33 4.17 3 5 1
## 3 2 1 25 4.62 0.485 0.0970 4.42 4.82 4.67 3.33 5.33 0.667
## 4 2 2 20 4.55 0.487 0.109 4.32 4.78 4.67 3.67 5.67 0.750# Immersion
myData %>% select(TUI_Immersion, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Immersion, na.rm = TRUE),
sd = sd(TUI_Immersion, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Immersion, na.rm = TRUE),
min=min(TUI_Immersion, na.rm = TRUE),
max=max(TUI_Immersion, na.rm = TRUE),
IQR=IQR(TUI_Immersion, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.06 1.56 0.349 3.33 4.79 4 1.5 6.75 2.38
## 2 1 2 24 3.80 1.50 0.305 3.17 4.43 3.5 1.5 6.25 2.31
## 3 2 1 25 3.91 1.43 0.287 3.32 4.50 4 1 6.5 1.75
## 4 2 2 20 4.19 1.38 0.309 3.54 4.83 4.38 1.5 7 1.56# Usefulness
myData_TUI_Nuetzlichkeit_noOutliers %>% select(TUI_Nuetzlichkeit, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Nuetzlichkeit, na.rm = TRUE),
sd = sd(TUI_Nuetzlichkeit, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Nuetzlichkeit, na.rm = TRUE),
min=min(TUI_Nuetzlichkeit, na.rm = TRUE),
max=max(TUI_Nuetzlichkeit, na.rm = TRUE),
IQR=IQR(TUI_Nuetzlichkeit, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.08 1.25 0.280 2.49 3.66 2.88 1 5.25 2.12
## 2 1 2 24 2.57 1.54 0.315 1.92 3.22 2.12 1 6.75 1.44
## 3 2 1 25 2.93 1.33 0.266 2.38 3.48 2.5 1 6 1.81
## 4 2 2 20 3.04 1.06 0.237 2.54 3.53 3 1.75 6 1.25# Skepticism
myData %>% select(TUI_Skepsis, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Skepsis, na.rm = TRUE),
sd = sd(TUI_Skepsis, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Skepsis, na.rm = TRUE),
min=min(TUI_Skepsis, na.rm = TRUE),
max=max(TUI_Skepsis, na.rm = TRUE),
IQR=IQR(TUI_Skepsis, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.05 1.10 0.246 2.54 3.56 3.25 1 5.25 0.812
## 2 1 2 24 2.81 1.13 0.231 2.33 3.29 3.12 1 4.5 1.62
## 3 2 1 25 2.51 1.13 0.226 2.04 2.98 2.25 1 4.75 2
## 4 2 2 20 2.9 1.34 0.300 2.27 3.53 2.5 1.25 5.5 2.12# Accessibility
myData_TUI_Zugaenglichkeit_noOutliers %>% select(TUI_Zugaenglichkeit, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(TUI_Zugaenglichkeit, na.rm = TRUE),
sd = sd(TUI_Zugaenglichkeit, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(TUI_Zugaenglichkeit, na.rm = TRUE),
min=min(TUI_Zugaenglichkeit, na.rm = TRUE),
max=max(TUI_Zugaenglichkeit, na.rm = TRUE),
IQR=IQR(TUI_Zugaenglichkeit, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.18 1.47 0.328 2.50 3.87 3.33 1 6.33 2.42
## 2 1 2 24 2.57 0.955 0.195 2.17 2.97 2.5 1 5 1.17
## 3 2 1 25 2.18 1.15 0.229 1.71 2.65 1.83 1 5 1.33
## 4 2 2 20 2.37 1.22 0.272 1.80 2.94 2.33 1 5 1.83Other questions - Concentration, etc.
Checking for outliers using Box-Plots
plot_Kontrolle <- ggplot(myData, aes(factor(Task_2DvAR, RvS), Kontrolle, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Control")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Konzentration <- ggplot(myData, aes(factor(Task_2DvAR), Konzentration, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Concentration")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Erfolg_Strategie <- ggplot(myData, aes(factor(Task_2DvAR), Erfolg_Strategie, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Perceived Success")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Spass <- ggplot(myData, aes(factor(Task_2DvAR), Spass, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Fun")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
#ggarrange(plot_Kontrolle, plot_Konzentration, plot_Erfolg_Strategie, plot_Spass,
#labels = c("Control", "Concentration", "Perceived Success", "Fun"),
#ncol = 2, nrow = 2)Removing outliers when Interquartile Range IQR > 1.5
Kontrolle_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Kontrolle_2D <- boxplot(Kontrolle_2D$Kontrolle, plot = FALSE)$out
# Replace the values with NA
Kontrolle_2D[Kontrolle_2D$Kontrolle %in% outliers_Kontrolle_2D, "Kontrolle"] = NA
Kontrolle_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Kontrolle_AR <- boxplot(Kontrolle_AR$Kontrolle, plot = FALSE)$out
# Replace the values with NA
Kontrolle_AR[Kontrolle_AR$Kontrolle %in% outliers_Kontrolle_AR, "Kontrolle"] = NA
myData_Kontrolle_noOutliers <- rbind(Kontrolle_2D, Kontrolle_AR)
Konzentration_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Konzentration_2D <- boxplot(Konzentration_2D$Konzentration, plot = FALSE)$out
# Replace the values with NA
Konzentration_2D[Konzentration_2D$Konzentration %in% outliers_Konzentration_2D, "Konzentration"] = NA
Konzentration_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Konzentration_AR <- boxplot(Konzentration_AR$Konzentration, plot = FALSE)$out
# Replace the values with NA
Konzentration_AR[Konzentration_AR$Konzentration %in% outliers_Konzentration_AR, "Konzentration"] = NA
myData_Konzentration_noOutliers <- rbind(Konzentration_2D, Konzentration_AR)
Erfolg_Strategie_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Erfolg_Strategie_2D <- boxplot(Erfolg_Strategie_2D$Erfolg_Strategie, plot = FALSE)$out
# Replace the values with NA
Erfolg_Strategie_2D[Erfolg_Strategie_2D$Erfolg_Strategie %in% outliers_Erfolg_Strategie_2D, "Erfolg_Strategie"] = NA
Erfolg_Strategie_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Erfolg_Strategie_AR <- boxplot(Erfolg_Strategie_AR$Erfolg_Strategie, plot = FALSE)$out
# Replace the values with NA
Erfolg_Strategie_AR[Erfolg_Strategie_AR$Erfolg_Strategie %in% outliers_Erfolg_Strategie_AR, "Erfolg_Strategie"] = NA
myData_Erfolg_Strategie_noOutliers <- rbind(Erfolg_Strategie_2D, Erfolg_Strategie_AR)
Spass_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Spass_2D <- boxplot(Spass_2D$Spass, plot = FALSE)$out
# Replace the values with NA
Spass_2D[Spass_2D$Spass %in% outliers_Spass_2D, "Spass"] = NA
Spass_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Spass_AR <- boxplot(Spass_AR$Spass, plot = FALSE)$out
# Replace the values with NA
Spass_AR[Spass_AR$Spass %in% outliers_Spass_AR, "Spass"] = NA
myData_Spass_noOutliers <- rbind(Spass_2D, Spass_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData_Kontrolle_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Kontrolle)$statistic,
`p-value` = shapiro.test(Kontrolle)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.911 0.0654
## 2 1 2 0.917 0.0507
## 3 2 1 0.958 0.369
## 4 2 2 0.915 0.0807myData_Konzentration_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Konzentration)$statistic,
`p-value` = shapiro.test(Konzentration)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.964 0.620
## 2 1 2 0.938 0.145
## 3 2 1 0.901 0.0194
## 4 2 2 0.933 0.176myData_Erfolg_Strategie_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Erfolg_Strategie)$statistic,
`p-value` = shapiro.test(Erfolg_Strategie)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.887 0.0240
## 2 1 2 0.941 0.170
## 3 2 1 0.919 0.0478
## 4 2 2 0.896 0.0341myData_Spass_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Spass)$statistic,
`p-value` = shapiro.test(Spass)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.900 0.0574
## 2 1 2 0.876 0.00821
## 3 2 1 0.762 0.0000570
## 4 2 2 0.847 0.00472#Perform QQ plots by group
ggplot(data = myData_Kontrolle_noOutliers, mapping = aes(sample = Kontrolle, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Konzentration_noOutliers, mapping = aes(sample = Konzentration, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Erfolg_Strategie_noOutliers, mapping = aes(sample = Erfolg_Strategie, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Spass_noOutliers, mapping = aes(sample = Spass, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_Kontrolle <- aov(Kontrolle ~ Task_2DvAR *RvS, data = myData_Kontrolle_noOutliers)
summary(ANOVA_Kontrolle)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 56.0 56.02 10.171 0.002 **
## RvS 1 2.7 2.72 0.494 0.484
## Task_2DvAR:RvS 1 0.3 0.33 0.060 0.807
## Residuals 85 468.2 5.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(ANOVA_Kontrolle)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.1013 0.3532
## 85effectsize::eta_squared(ANOVA_Kontrolle, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.11 | [0.03, 1.00]
## RvS | 5.77e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 7.05e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_Konzentration <- aov(Konzentration ~ Task_2DvAR *RvS, data = myData_Konzentration_noOutliers)
summary(ANOVA_Konzentration)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 13.7 13.651 2.390 0.126
## RvS 1 5.8 5.833 1.021 0.315
## Task_2DvAR:RvS 1 0.1 0.069 0.012 0.913
## Residuals 85 485.5 5.712leveneTest(ANOVA_Konzentration)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.6032 0.1947
## 85ANOVA_Erfolg_Strategie <- aov(Erfolg_Strategie ~ Task_2DvAR *RvS, data = myData_Erfolg_Strategie_noOutliers)
summary(ANOVA_Erfolg_Strategie)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 57.6 57.64 8.259 0.00512 **
## RvS 1 0.7 0.66 0.094 0.75945
## Task_2DvAR:RvS 1 5.5 5.54 0.794 0.37539
## Residuals 85 593.2 6.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(ANOVA_Erfolg_Strategie)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.4438 0.2358
## 85effectsize::eta_squared(ANOVA_Erfolg_Strategie, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.09 | [0.02, 1.00]
## RvS | 1.11e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 9.26e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_Spass <- aov(Spass ~ Task_2DvAR *RvS, data = myData_Spass_noOutliers)
summary(ANOVA_Spass)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 0.9 0.905 0.133 0.716
## RvS 1 0.0 0.048 0.007 0.933
## Task_2DvAR:RvS 1 2.4 2.423 0.356 0.552
## Residuals 82 557.9 6.804
## 3 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Spass)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3708 0.7743
## 82Descriptive statistic per group
# Control
myData_Kontrolle_noOutliers %>% select(Kontrolle, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Kontrolle, na.rm = TRUE),
sd = sd(Kontrolle, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Kontrolle, na.rm = TRUE),
min=min(Kontrolle, na.rm = TRUE),
max=max(Kontrolle, na.rm = TRUE),
IQR=IQR(Kontrolle, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 3.85 2.28 0.509 2.78 4.92 4 0 7 4.25
## 2 1 2 24 3.38 1.79 0.365 2.62 4.13 3 1 7 2.25
## 3 2 1 25 5.28 2.70 0.540 4.17 6.39 6 0 10 3
## 4 2 2 20 5.05 2.52 0.564 3.87 6.23 5 1 9 4.25# Concentration
myData_Konzentration_noOutliers %>% select(Konzentration, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Konzentration, na.rm = TRUE),
sd = sd(Konzentration, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Konzentration, na.rm = TRUE),
min=min(Konzentration, na.rm = TRUE),
max=max(Konzentration, na.rm = TRUE),
IQR=IQR(Konzentration, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 5.5 2.65 0.592 4.26 6.74 6 0 10 3
## 2 1 2 24 5.96 2.27 0.464 5.00 6.92 6 1 9 3.25
## 3 2 1 25 6.28 2.75 0.549 5.15 7.41 7 1 10 5
## 4 2 2 20 6.85 1.66 0.372 6.07 7.63 7 4 10 2.25# Perceived Success
myData_Erfolg_Strategie_noOutliers %>% select(Erfolg_Strategie, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Erfolg_Strategie, na.rm = TRUE),
sd = sd(Erfolg_Strategie, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Erfolg_Strategie, na.rm = TRUE),
min=min(Erfolg_Strategie, na.rm = TRUE),
max=max(Erfolg_Strategie, na.rm = TRUE),
IQR=IQR(Erfolg_Strategie, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 4.75 2.75 0.615 3.46 6.04 6 1 9 5
## 2 1 2 24 4.42 2.41 0.492 3.40 5.44 5 0 9 3.25
## 3 2 1 25 5.88 3.15 0.631 4.58 7.18 7 0 10 5
## 4 2 2 20 6.55 2.01 0.45 5.61 7.49 7 3 9 3# Fun
myData_Spass_noOutliers %>% select(Spass, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Spass, na.rm = TRUE),
sd = sd(Spass, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Spass, na.rm = TRUE),
min=min(Spass, na.rm = TRUE),
max=max(Spass, na.rm = TRUE),
IQR=IQR(Spass, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 20 7.61 2.06 0.461 6.65 8.58 7 3 10 2.75
## 2 1 2 24 7.30 2.53 0.517 6.24 8.37 8 2 10 3
## 3 2 1 25 7.48 3.15 0.630 6.18 8.78 9 1 10 4
## 4 2 2 20 7.85 2.37 0.530 6.74 8.96 8.5 2 10 4Pain / Discomfort
Checking for outliers using Box-Plots
plot_Pain <- ggplot(myData, aes(factor(Task_2DvAR), Pain, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Pain")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Pressure_on_head <- ggplot(myData, aes(factor(Task_2DvAR), Pressure_on_head, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Pressure on Head")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Headache <- ggplot(myData, aes(factor(Task_2DvAR), Headache, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Headache")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
plot_Eye_burning <- ggplot(myData, aes(factor(Task_2DvAR), Eye_burning, )) + geom_boxplot() +
geom_boxplot(fill = "grey92") +
geom_point(aes(colour = Task_2DvAR),
## draw bigger points
size = 2,
## add some transparency
alpha = .5, show.legend = FALSE,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)) +
scale_fill_brewer(palette = "Dark2",
) +
theme_classic() +
scale_x_discrete(name = "Condition",
labels = c("2D", "AR")) +
scale_y_continuous(name = "Eye Burning")+
theme(axis.text.x = element_text(color = "grey20", size = 8, angle = 0, hjust = .5, vjust = .5, face = "plain"),
axis.text.y = element_text(color = "grey20", size = 8, angle = 0, hjust = 1, vjust = 0, face = "plain"),
axis.title.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = 0, face = "plain"),
axis.title.y = element_text(color = "grey20", size = 10, angle = 90, hjust = .5, vjust = .5, face = "plain"))
ggarrange(plot_Pain, plot_Pressure_on_head, plot_Headache, plot_Eye_burning,
# labels = c("Pain", "Pressure on Head", "Headache", "Eye Burning"),
ncol = 2, nrow = 2)
Removing outliers when Interquartile Range IQR > 1.5
Pain_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Pain_2D <- boxplot(Pain_2D$Pain, plot = FALSE)$out
# Replace the values with NA
Pain_2D[Pain_2D$Pain %in% outliers_Pain_2D, "Pain"] = NA
Pain_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Pain_AR <- boxplot(Pain_AR$Pain, plot = FALSE)$out
# Replace the values with NA
Pain_AR[Pain_AR$Pain %in% outliers_Pain_AR, "Pain"] = NA
myData_Pain_noOutliers <- rbind(Pain_2D, Pain_AR)
Pressure_on_head_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Pressure_on_head_2D <- boxplot(Pressure_on_head_2D$Pressure_on_head, plot = FALSE)$out
# Replace the values with NA
Pressure_on_head_2D[Pressure_on_head_2D$Pressure_on_head %in% outliers_Pressure_on_head_2D, "Pressure_on_head"] = NA
Pressure_on_head_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Pressure_on_head_AR <- boxplot(Pressure_on_head_AR$Pressure_on_head, plot = FALSE)$out
# Replace the values with NA
Pressure_on_head_AR[Pressure_on_head_AR$Pressure_on_head %in% outliers_Pressure_on_head_AR, "Pressure_on_head"] = NA
myData_Pressure_on_head_noOutliers <- rbind(Pressure_on_head_2D, Pressure_on_head_AR)
Headache_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Headache_2D <- boxplot(Headache_2D$Headache, plot = FALSE)$out
# Replace the values with NA
Headache_2D[Headache_2D$Headache %in% outliers_Headache_2D, "Headache"] = NA
Headache_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Headache_AR <- boxplot(Headache_AR$Headache, plot = FALSE)$out
# Replace the values with NA
Headache_AR[Headache_AR$Headache %in% outliers_Headache_AR, "Headache"] = NA
myData_Headache_noOutliers <- rbind(Headache_2D, Headache_AR)
Eye_burning_2D <- subset(myData[myData$Task_2DvAR == 1, ], )
# create a vector of outliers for the numeric factor
outliers_Eye_burning_2D <- boxplot(Eye_burning_2D$Eye_burning, plot = FALSE)$out
# Replace the values with NA
Eye_burning_2D[Eye_burning_2D$Eye_burning %in% outliers_Eye_burning_2D, "Eye_burning"] = NA
Eye_burning_AR <- subset(myData[myData$Task_2DvAR == 2, ], )
# create a vector of outliers for the numeric factor
outliers_Eye_burning_AR <- boxplot(Eye_burning_AR$Eye_burning, plot = FALSE)$out
# Replace the values with NA
Eye_burning_AR[Eye_burning_AR$Eye_burning %in% outliers_Eye_burning_AR, "Eye_burning"] = NA
myData_Eye_burning_noOutliers <- rbind(Eye_burning_2D, Eye_burning_AR)Checking normal distribution
#Perform the Shapiro-Wilk Test for Normality on each group
myData_Pain_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Pain)$statistic,
`p-value` = shapiro.test(Pain)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.765 0.000502
## 2 1 2 0.838 0.00212
## 3 2 1 0.793 0.000511
## 4 2 2 0.650 0.0000149myData_Pressure_on_head_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Pressure_on_head)$statistic,
`p-value` = shapiro.test(Pressure_on_head)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.890 0.0390
## 2 1 2 0.959 0.415
## 3 2 1 0.891 0.0163
## 4 2 2 0.818 0.00272myData_Headache_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Headache)$statistic,
`p-value` = shapiro.test(Headache)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.849 0.00653
## 2 1 2 0.755 0.000108
## 3 2 1 0.533 0.00000156
## 4 2 2 0.591 0.00000362myData_Eye_burning_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarise(`W Statistic` = shapiro.test(Eye_burning)$statistic,
`p-value` = shapiro.test(Eye_burning)$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 4
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS `W Statistic` `p-value`
## <fct> <fct> <dbl> <dbl>
## 1 1 1 0.746 0.000400
## 2 1 2 0.742 0.0000524
## 3 2 1 0.677 0.00000707
## 4 2 2 0.625 0.0000283#Perform QQ plots by group
ggplot(data = myData_Pain_noOutliers, mapping = aes(sample = Pain, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Pressure_on_head_noOutliers, mapping = aes(sample = Pressure_on_head, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Headache_noOutliers, mapping = aes(sample = Headache, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
ggplot(data = myData_Eye_burning_noOutliers, mapping = aes(sample = Eye_burning, color = Task_2DvAR, fill = Task_2DvAR)) +
#stat_qq_band(alpha=0.5, conf=0.95, qtype=1, bandType = "ts") +
stat_qq_line(identity=TRUE) +
stat_qq_point(col="black") +
facet_wrap(~ Task_2DvAR, scales = "free") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() 
To compare groups (2D vs AR) and conditions (real vs sham) we are calculating 2x2 ANOVAs
ANOVA_Pain <- aov(Pain ~ Task_2DvAR *RvS, data = myData_Pain_noOutliers)
summary(ANOVA_Pain)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 18.0 18.050 2.153 0.146
## RvS 1 0.1 0.141 0.017 0.897
## Task_2DvAR:RvS 1 19.7 19.663 2.345 0.130
## Residuals 76 637.1 8.383
## 9 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Pain)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.9452 0.1294
## 76ANOVA_Pressure_on_head <- aov(Pressure_on_head ~ Task_2DvAR *RvS, data = myData_Pressure_on_head_noOutliers)
summary(ANOVA_Pressure_on_head)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 530 530.3 2.274 0.136
## RvS 1 11 11.0 0.047 0.828
## Task_2DvAR:RvS 1 26 25.6 0.110 0.741
## Residuals 79 18425 233.2
## 6 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Pressure_on_head)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.6285 0.5988
## 79ANOVA_Headache <- aov(Headache ~ Task_2DvAR *RvS, data = myData_Headache_noOutliers)
summary(ANOVA_Headache)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 74.8 74.80 4.827 0.0312 *
## RvS 1 5.4 5.42 0.350 0.5562
## Task_2DvAR:RvS 1 16.0 15.96 1.030 0.3135
## Residuals 74 1146.8 15.50
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Headache)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.7318 0.1678
## 74effectsize::eta_squared(ANOVA_Headache, partial = TRUE)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## Task_2DvAR | 0.06 | [0.00, 1.00]
## RvS | 4.70e-03 | [0.00, 1.00]
## Task_2DvAR:RvS | 0.01 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].ANOVA_Eye_burning <- aov(Eye_burning ~ Task_2DvAR *RvS, data = myData_Eye_burning_noOutliers)
summary(ANOVA_Eye_burning)## Df Sum Sq Mean Sq F value Pr(>F)
## Task_2DvAR 1 205 204.65 2.683 0.106
## RvS 1 17 16.79 0.220 0.640
## Task_2DvAR:RvS 1 64 63.88 0.838 0.363
## Residuals 75 5720 76.27
## 10 Beobachtungen als fehlend gelöschtleveneTest(ANOVA_Eye_burning)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.48 0.2268
## 75Descriptive statistic per group
# Pain
myData_Pain_noOutliers %>% select(Pain, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Pain, na.rm = TRUE),
sd = sd(Pain, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Pain, na.rm = TRUE),
min=min(Pain, na.rm = TRUE),
max=max(Pain, na.rm = TRUE),
IQR=IQR(Pain, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 1 1 20 2.22 3.00 0.671 0.818 3.63 1 0 10 3.5
## 2 1 2 24 3.14 3.40 0.694 1.70 4.57 2 0 10 5
## 3 2 1 25 2.29 2.94 0.587 1.07 3.50 1 0 10 5
## 4 2 2 20 1.21 1.96 0.438 0.293 2.13 1 0 8 1.5# Pressure on Head
myData_Pressure_on_head_noOutliers %>% select(Pressure_on_head, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Pressure_on_head, na.rm = TRUE),
sd = sd(Pressure_on_head, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Pressure_on_head, na.rm = TRUE),
min=min(Pressure_on_head, na.rm = TRUE),
max=max(Pressure_on_head, na.rm = TRUE),
IQR=IQR(Pressure_on_head, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 1 1 20 20.5 17.5 3.92 12.3 28.7 19.5 0 50 25.5
## 2 1 2 24 20.9 13.2 2.69 15.3 26.4 18 0 55 19.2
## 3 2 1 25 16.5 15.4 3.08 10.1 22.8 13 0 57 15.5
## 4 2 2 20 14.6 15.3 3.42 7.45 21.8 10 0 50 16.5# Headache
myData_Headache_noOutliers %>% select(Headache, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Headache, na.rm = TRUE),
sd = sd(Headache, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Headache, na.rm = TRUE),
min=min(Headache, na.rm = TRUE),
max=max(Headache, na.rm = TRUE),
IQR=IQR(Headache, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 1 1 20 3.79 3.72 0.832 2.05 5.53 2 0 10
## 2 1 2 24 3.45 4.65 0.949 1.49 5.42 1.5 0 15
## 3 2 1 25 0.889 1.97 0.393 0.0769 1.70 0 0 7
## 4 2 2 20 2.37 4.59 1.03 0.223 4.51 0 0 16
## # ℹ 1 more variable: IQR <dbl># Eye Burning
myData_Eye_burning_noOutliers %>% select(Eye_burning, Task_2DvAR, RvS) %>% group_by(Task_2DvAR, RvS) %>%
summarise(n = n(),
mean = mean(Eye_burning, na.rm = TRUE),
sd = sd(Eye_burning, na.rm = TRUE),
stderr = sd/sqrt(n),
LCL = mean - qt(1 - (0.05 / 2), n - 1) * stderr,
UCL = mean + qt(1 - (0.05 / 2), n - 1) * stderr,
median=median(Eye_burning, na.rm = TRUE),
min=min(Eye_burning, na.rm = TRUE),
max=max(Eye_burning, na.rm = TRUE),
IQR=IQR(Eye_burning, na.rm = TRUE))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 12
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS n mean sd stderr LCL UCL median min max IQR
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 1 1 20 5.88 8.20 1.83 2.04 9.72 2 0 30 10
## 2 1 2 24 8.61 11.3 2.31 3.82 13.4 3 0 30 14
## 3 2 1 25 4.61 7.37 1.47 1.57 7.65 1 0 25 5
## 4 2 2 20 3.69 6.44 1.44 0.675 6.70 1 0 25 3.5Correlations between questionnaires and NF performance (individual regression slopes)
#SSQ - Nausea
myData_SSQ_diff_N_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor.test(SSQ_diff_N,slope, use = "complete.obs")$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 0.0711
## 2 1 2 0.346
## 3 2 1 0.134
## 4 2 2 0.645myData_SSQ_diff_N_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor(SSQ_diff_N,slope, use = "complete.obs"))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 -0.423
## 2 1 2 -0.206
## 3 2 1 -0.308
## 4 2 2 -0.113#Flow - Fluency and concern
myData_Flow_Glatter_Verlauf_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor.test(Flow_Glatter_Verlauf,slope, use = "complete.obs")$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 0.208
## 2 1 2 0.652
## 3 2 1 0.894
## 4 2 2 0.364myData_Flow_Glatter_Verlauf_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor(Flow_Glatter_Verlauf,slope, use = "complete.obs"))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 -0.294
## 2 1 2 -0.0972
## 3 2 1 0.0280
## 4 2 2 0.214myData_Flow_Besorgnis_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(Flow_Besorgnis,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.901
## 2 2 0.771myData_Flow_Besorgnis_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(Flow_Besorgnis,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.0193
## 2 2 -0.0463myData_Flow_Total_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(Flow_Besorgnis,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.901
## 2 2 0.966myData_Flow_Total_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(Flow_Besorgnis,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.0193
## 2 2 0.00654#TUI - Technology anxiety, usability and accessibility
myData_TUI_Technologieaengstlichkeit_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(TUI_Technologieaengstlichkeit,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.245
## 2 2 0.825myData_TUI_Technologieaengstlichkeit_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(TUI_Technologieaengstlichkeit,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 -0.179
## 2 2 0.0344myData_TUI_Benutzerfreundlichkeit_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(TUI_Benutzerfreundlichkeit,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.398
## 2 2 0.616myData_TUI_Benutzerfreundlichkeit_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(TUI_Benutzerfreundlichkeit,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 -0.131
## 2 2 -0.0786myData_TUI_Zugaenglichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor.test(TUI_Zugaenglichkeit,slope, use = "complete.obs")$p.value)## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 0.469
## 2 1 2 0.512
## 3 2 1 0.00318
## 4 2 2 0.237myData_TUI_Zugaenglichkeit_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor(TUI_Zugaenglichkeit,slope, use = "complete.obs"))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 0.172
## 2 1 2 0.141
## 3 2 1 0.577
## 4 2 2 -0.285#Other questionnaires - Control
myData_Kontrolle_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(Kontrolle,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.765
## 2 2 0.243myData_Kontrolle_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(Kontrolle,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 -0.0464
## 2 2 0.178myData_Erfolg_Strategie_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(Erfolg_Strategie,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.173
## 2 2 0.556myData_Erfolg_Strategie_noOutliers %>%
group_by(Task_2DvAR, RvS) %>%
summarize(test=cor(Erfolg_Strategie,slope, use = "complete.obs"))## `summarise()` has grouped output by 'Task_2DvAR'. You can override using the
## `.groups` argument.## # A tibble: 4 × 3
## # Groups: Task_2DvAR [2]
## Task_2DvAR RvS test
## <fct> <fct> <dbl>
## 1 1 1 -0.303
## 2 1 2 -0.133
## 3 2 1 -0.0888
## 4 2 2 0.383#Physical condition
myData_Headache_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor.test(Headache,slope, use = "complete.obs")$p.value)## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 0.563
## 2 2 0.627myData_Headache_noOutliers %>%
group_by(Task_2DvAR) %>%
summarize(test=cor(Headache,slope, use = "complete.obs"))## # A tibble: 2 × 2
## Task_2DvAR test
## <fct> <dbl>
## 1 1 -0.0931
## 2 2 0.0826